|TensorFlow 1 version|
tf.image module contains various functions for image
processing and decoding-encoding Ops.
Many of the encoding/decoding functions are also available in the
The resizing Ops accept input images as tensors of several types. They always output resized images as float32 tensors.
The convenience function
tf.image.resize supports both 4-D
and 3-D tensors as input and output. 4-D tensors are for batches of images,
3-D tensors for individual images.
Resized images will be distorted if their original aspect ratio is not the same as size. To avoid distortions see tf.image.resize_with_pad.
tf.image.ResizeMethod provides various resize methods like
Converting Between Colorspaces
Image ops work either on individual images or on batches of images, depending on the shape of their input Tensor.
If 3-D, the shape is
[height, width, channels], and the Tensor represents one
image. If 4-D, the shape is
[batch_size, height, width, channels], and the
channels can usefully be 1, 2, 3, or 4. Single-channel images are
grayscale, images with 3 channels are encoded as either RGB or HSV. Images
with 2 or 4 channels include an alpha channel, which has to be stripped from the
image before passing the image to most image processing functions (and can be
Internally, images are either stored in as one
float32 per channel per pixel
(implicitly, values are assumed to lie in
[0,1)) or one
uint8 per channel
per pixel (values are assumed to lie in
TensorFlow can convert between images in RGB or HSV or YIQ.
TensorFlow provides functions to adjust images in various ways: brightness, contrast, hue, and saturation. Each adjustment can be done with predefined parameters or with random parameters picked from predefined intervals. Random adjustments are often useful to expand a training set and reduce overfitting.
If several adjustments are chained it is advisable to minimize the number of redundant conversions by first converting the images to the most natural data type and representation.